Summary
Boshi Yuan is a PhD candidate in computer science at Shanghai Jiao Tong University with eight years of industry and research experience focused on cryptography, multi-party computation, and cybersecurity. His recent USENIX Security 2024 paper, "MD-ML: Super Fast Privacy-Preserving Machine Learning for Malicious Security with a Dishonest Majority," highlights practical advances in secure ML under adversarial settings. He combines deep theoretical knowledge with hands-on protocol design, aiming to make privacy-preserving systems both efficient and deployable. Based in Shanghai, Boshi bridges academic rigor and applied research, often exploring performance optimizations that make cryptographic primitives usable in real-world workflows.
8 years of coding experience
上海交通大学